arXiv (All)
open access
Total Simulated Survey Error: Designing and Diagnosing Survey Responses from Large Language Models
Large Language models (LLMs), having been trained on vast amounts of human-generated data, may encode the attitudes and behaviors of these humans. As such, LLMs show promise in mimicking human-like patterns that facilitate their use in simulating people in a wide variety of contexts. One such contex
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation#984165
- MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation#987481
- What Kind of Reasoning (if any) is an LLM actually doing? On the Stochastic Nature and Abductive Appearance of Large Language Models#701151
- WASIL: In-the-Wild Arabic Spoken Interactions with LLMs#1143765
- Contextual Contamination- The Silent Drift of Large Language Models via Stored Conversation Data#94408
Record · ID 997177
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive